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dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorYang, Xin-She
dc.contributor.authorIglesias, Andres
dc.contributor.authorGalvez, Akemi
dc.date.accessioned2020-07-14T15:16:51Z
dc.date.available2020-07-14T15:16:51Z
dc.date.issued2020
dc.identifier.citationOsaba E., Del Ser J., Yang XS., Iglesias A., Galvez A. (2020) COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking. In: Krzhizhanovskaya V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12141. Springer, Chamen
dc.identifier.isbn978-3-030-50425-0en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/11556/944
dc.description.abstractMultitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.en
dc.description.sponsorshipEneko Osaba and Javier Del Ser would like to thank the Basque Government for its support through the EMAITEK and ELKARTEK programs. Javier Del Ser receives support from the Consolidated Research Group MATHMODE (IT1294-9) granted by the Department of Education of the Basque Government. Andres Iglesias and Akemi Galvez thank the Computer Science National Program of the Spanish Research Agency and European Funds, Project #TIN2017-89275-R (AEI/FEDER, UE), and the PDE-GIR project of the European Union’s Horizon 2020 programme, Marie Sklodowska-Curie Actions grant agreement #778035.en
dc.language.isoengen
dc.publisherSpringer Natureen
dc.titleCOEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitaskingen
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-030-50426-7_19en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIRen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsTransfer optimizationen
dc.subject.keywordsEvolutionary multitaskingen
dc.subject.keywordsBat algorithmen
dc.subject.keywordsMultifactorial optimizationen
dc.subject.keywordsTraveling salesman problemen
dc.identifier.essn1611-3349en
dc.journal.titleLecture Notes in Computer Scienceen
dc.page.final256en
dc.page.initial244en
dc.volume.number12141en
dc.identifier.esbn978-3-030-50426-7en
dc.conference.title20th International Conference on Computational Science, ICCS 2020; Amsterdam; Netherlands; 3 June 2020 through 5 June 2020en


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